The AI Native Dev - from Copilot today to AI Native Software Development tomorrow
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134 afleveringen, van nieuw naar oud
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Maggie Appleton: Agentic Coding Has Outgrown the Text Box
3 november 2026 · 52 min
Agentic coding has outgrown the text box. Maggie Appleton, staff research engineer at GitHub Next, argues that walls of CLI output and AGENTS.md files serve coding agents far better than the humans steering them. She walks through Chopin, the GitHub Next prototype where background agents turn a team's planning chat into a structured plan, and makes the case for agents that prompt you instead of waiting to be prompted. Lees meer
What we cover:
– Why text-heavy agentic coding tools favor agents over humans
– Boundary objects: what AGENTS.md, skill files and MCP servers get right and wrong
– Multiplayer planning with coding agents in GitHub Next's Chopin
– Human-to-agent handoffs, formal verification and spec-driven development
– Why software quality is rising, not falling, with coding agents
– Which roles still matter: PMs, designers and engineersChapters:
00:00:00 - Introduction
00:02:14 - Inside GitHub Next and the Chopin project
00:05:57 - Why solo agents break team alignment
00:07:52 - Why coding agents struggle with design
00:12:16 - Boundary objects: AGENTS.md, skills and MCP
00:18:27 - Planning with coding agents as collaborators
00:26:49 - Multiplayer planning with background agents
00:31:57 - Agent handoffs and spec-driven development
00:37:41 - Quality, craft and who decides what to build
00:44:29 - Starting a career in agentic codingBuild your software factory, one workflow at a time, with Tessl:
https://tessl.co/j3d🔔 Subscribe for weekly episodes on AI-native development
Should coding agents be prompting you more? Tell us in the comments how your team plans agentic coding work today.
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Caleb Sima: AI Attackers Move at Machine Speed
27 oktober 2026 · 55 min
AI attackers now move at machine speed, while most security teams still move at meeting speed. Former Robinhood CSO Caleb Sima has seen an AI attacker up close, and he argues the only way to keep up is to take humans out of the defense loop. Lees meer
Caleb joins Guy Podjarny to unpack what changes when the attacker is an agent: one that hits every surface at once, reverses open-source code to find a 0-day, and once inside, burns your own AI tokens to keep going.
What we cover:
– Why AI attackers outran the red teamers' predictions, including 400 Linux 0-days found at 2.5 hours each
– Whether sandboxes can contain AI agents, and why external monitoring matters
– Who is accountable when your AI agent hacks someone else
– What actually changes when an AI attacker gets inside your company
– Deception, digital twins and forensic-grade detection
– Automating security: instant patching, one-way vs two-way doors, and security teams that ship the fixChapters:
00:00:00 - Introduction
00:02:58 - AI security goes mainstream
00:05:52 - Why AI attackers surprised the red teamers
00:12:50 - Can sandboxes contain AI agents?
00:18:03 - Security vs safety vs alignment
00:22:18 - Who's accountable when your AI agent hacks?
00:29:00 - What changes when an AI attacker hits you
00:36:47 - Detection, forensics and deception
00:44:10 - Machine speed vs meeting speed: automating defense
00:48:53 - Security teams that ship the fixBuild your software factory, one workflow at a time, with Tessl:
https://tessl.co/9ja🔔 Subscribe for weekly episodes on AI-native development
Are your defenses ready for AI attackers, or still running at meeting speed? Tell us in the comments.
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Your Coding Agent Should Never Hold a Credential
22 oktober 2026 · 30 min
Your coding agent should never hold a credential, and in a software factory that rule is only the start. Recorded live at the AI Security Summit in San Francisco, Simon Maple sits down with Eli Aleyner (Docker), Marcelo Sousa (Snyk) and Ian Livingstone (Keycard), co-authors of Agent Baseline, a security reference architecture for the AI software factory. Lees meer
The panel unpacks why agents have to be treated as fundamentally unpredictable actors, why sandboxing alone is not enough when skills and MCP servers make the supply chain dynamic, and why approval prompts collapse into fatigue. Marcelo's verdict: sometimes these are kids with chainsaws.
What we cover:
– The three levels of autonomy, from coding agents to fully agent-directed workflows
– Sandboxing coding agents and injecting secrets at the egress point
– Why the agent supply chain is dynamic: skills, MCP servers and cloned repos
– Prompt injection, least privilege and agents monitoring other agents in the harness
– Approval fatigue: YOLO mode vs CYA mode
– Where to start securing your software factory on Monday morningChapters:
00:00:00 - Introduction
00:03:14 - What Agent Baseline is and who it's for
00:04:17 - The three levels of software factory autonomy
00:06:53 - Sandbox boundaries and manufacturing trust in agents
00:09:22 - Why coding agents should never hold credentials
00:11:44 - The dynamic agent supply chain: skills and MCP servers
00:14:57 - Untrusted input, least privilege and agents watching agents
00:18:02 - Approval fatigue: YOLO mode vs CYA mode
00:20:22 - Are poisoned agent skills a supply chain problem?
00:22:45 - Where the panel disagreed, and where to start MondayBuild your software factory, one workflow at a time, with Tessl:
https://tessl.co/gsh🔔 Subscribe for weekly episodes on AI-native development
Which level of the software factory are you at today, and what are you locking down first? Tell us in the comments.
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Snyk's Manoj Nair: Skills and MCP Are the New npm
20 oktober 2026 · 47 min
Skills and MCP servers are the new npm, and AI coding agents are pulling them into enterprise codebases faster than security can keep up. Manoj Nair, Chief Innovation Officer at Snyk, joins Guy Podjarny at the Snyk AI Security Summit to map where enterprise agentic coding really stands. Lees meer
They get into why a 50,000-agent Claude Code rollout was pulled forward by weeks, why the autonomous attacker has some CISOs ready to accept an outage to patch faster, and how security is moving into the agent loop of the software factory.
What we cover:
– Why leadership and culture, not tooling, decide how fast enterprises adopt AI coding agents
– The three risks of agentic coding: insecure output, the skills and MCP server supply chain, and agent behavior
– Running coding agents locked down: sandboxes, agent identities and no open internet
– Who owns the code an agent writes, and why AI-native organizations treat it as shared risk
– Securing the software factory: security context in the agent loop and agentic remediation of the backlog
– What still has to be solved before enterprises trust agents: identity, intent and behaviorChapters:
00:00:00 - Introduction
00:01:50 - The state of enterprise AI and agent adoption
00:09:45 - Leadership, culture and a 50,000-agent rollout
00:11:18 - Agentic coding risks: output, supply chain, behavior
00:14:29 - Locked-down coding agents and the security trade-off
00:18:01 - Who owns agent output? Compliance and accountability
00:22:54 - The autonomous attacker and security technical debt
00:27:56 - Securing the product vs securing the software factory
00:33:00 - Agentic remediation and the security backlog
00:40:18 - What enterprises need to trust AI coding agentsBuild your software factory, one workflow at a time, with Tessl:
https://tessl.co/jw0🔔 Subscribe for weekly episodes on AI-native development
Where is your organization on the path from copilot to AI coding agents? Tell us in the comments.
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Maria Gorinova: Feedback Loops That Fix Your Codebase
13 oktober 2026 · 38 min
Agent feedback loops are how a software factory maintains itself: agents mining their own session logs, PR comments and production signals, then fixing what they find. Maria Gorinova, member of technical staff at Tessl, explains how to build them, and why vague intent turns a loop into a mess. Lees meer
What we cover:
– What a software factory is: repeatable workflows, a shared execution environment and agent feedback loops
– Why agent context alone isn't enough, and where harness engineering comes in
– The three stages of a feedback loop: analyze a data stream, find the problem, request the fix
– Inner, middle and outer loops, from verifiers on every PR to self-healing in production
– How Tessl's "comment diet" loop catches drift that no single PR causes
– Operators and stewards: the human roles in a software factoryChapters:
00:00:00 - Introduction
00:02:36 - What is a software factory?
00:05:38 - From agents to context to harness engineering
00:09:15 - The three stages of an agent feedback loop
00:13:16 - Inner, middle and outer feedback loops
00:18:35 - Why vague intent breaks agent feedback loops
00:21:50 - Comment bloat, drift and the rule of three
00:25:48 - The software factory mindset
00:27:45 - Loops, feedback loops and meta feedback loops
00:32:45 - Operators, stewards and building your first loopBuild your software factory, one workflow at a time, with Tessl:
https://tessl.co/2uf🔔 Subscribe for weekly episodes on AI-native development
Which agent feedback loop would you build first in your own software factory? Tell us in the comments.
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Legora CTO: Why AI Agents Need More Than a Chat Window
6 oktober 2026 · 44 min
AI coding agents now write almost all of Legora's code, with engineers running up to ten in parallel. Yet the CTO behind that shift thinks measuring adoption by tokens burned is a mistake. Jacob Lauritzen, CTO of Legora, joins Simon Maple to unpack what actually scales when agents do the typing, and where human judgment still has to stay in charge. Lees meer
What we cover:
– How Legora's engineers orchestrate many AI coding agents at once, from shared local dev resources to cloud agent setups
– An agentic loop that reproduces, fixes and tests bugs straight from Slack
– Why token maxing is a bad way to drive AI adoption, and what to measure instead
– Agent evaluation: regression testing the harness as models and prompts change every week
– Decision models, the verifier's rule, and where autonomy ends and human judgment begins
– Why AI agents need more than a chat interfaceChapters:
00:00:00 - Introduction
00:01:47 - Legora's growth from $100M to $200M ARR
00:03:44 - Parallel coding agents and bug-fix loops
00:07:15 - Senior engineers vs AI-first engineers
00:10:10 - Why token maxing is the new lines of code
00:14:13 - What AI can and can't verify in legal work
00:17:30 - Decision models vs autoregressive LLMs
00:24:22 - Weekly model benchmarks and harness evals
00:26:58 - Why AI agents need more than a chat interface
00:35:03 - The verifier's rule and litigation strategyBuild your software factory, one workflow at a time, with Tessl:
https://tessl.co/lpg🔔 Subscribe for weekly episodes on AI-native development
Is token count telling you anything real about how your team uses AI coding agents? Tell us in the comments.
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Liz Fong-Jones: 2x the PRs, 1.5x the Incidents
29 september 2026 · 50 min
Honeycomb went from 30 to 70 merged pull requests a day in three months. The catch: automated code review, not code generation, became the real bottleneck of software automation. Liz Fong-Jones, Technical Fellow at Honeycomb, explains why incidents still rose 1.5x, how their internal bot Autobot now reviews every PR, and why AI amplifies whatever org you already have. Lees meer
What we cover:
– How automated code review lets humans focus on design, not trivial bugs
– Using a decision model like Jev to decide which PRs are safe to auto-merge
– What makes a codebase ready for AI coding agents
– When to trust AI agents with production incidents, and when they're just throwing darts
– Why "Claude did it" isn't an excuse, and what ownership means with AI
– How open source maintainers can handle a flood of AI slop pull requestsChapters:
00:00:00 - Introduction
00:06:41 - Why AI amplifies dysfunctional engineering orgs
00:10:45 - What makes a codebase AI ready
00:14:01 - Honeycomb's Autobot and automated code review
00:19:06 - Using Jev to decide which PRs are safe to merge
00:26:51 - Trusting AI agents during production incidents
00:30:40 - Least privilege and guardrails for coding agents
00:33:25 - If your name's on it, you own it
00:38:55 - AI slop pull requests and open source
00:45:56 - Will observability engineering survive as a role?Build your software factory, one workflow at a time, with Tessl:
https://tessl.co/nlr🔔 Subscribe for weekly episodes on AI-native development
Is your team's review capacity keeping up with your AI coding agents? Tell us in the comments.
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Dexter Horthy: Why We Stopped Trusting AI to Write the Plan
22 september 2026 · 56 min
HumanLayer CEO Dexter Horthy on why his team's bet on spec driven development nearly wrecked their own codebase, and what changed his mind about reviewing AI-written code. Dexter walks through the "YOLO pull request" experiment that ran for months before the codebase became unusable, the "dumb zone" that limits how much context you can hand a model, and why he now believes there will always be alpha in reviewing something. Lees meer
What we cover:
– How HumanLayer's "read the plan, skip the code" experiment quietly broke their own codebase
– Why context windows have a "dumb zone," and what that means for context engineering
– Sean Grove's idea that specs, not code, are becoming the durable artifact
– Building overnight agents that review and fix code before a human ever sees it
– Why treating software development like a factory changes how bugs compound
– What Dexter thinks his advice on reviewing AI-written code will look like in 12 monthsChapters:
00:00:00 - Introduction
00:02:15 - Meet Dexter Horthy, CEO of HumanLayer
00:06:03 - The "dumb zone": why more context makes models dumber
00:06:40 - Sean Grove's "the spec is the new code"
00:09:22 - The YOLO pull-request experiment that broke their codebase
00:11:09 - Letting the model own the architecture
00:17:03 - Planning as expected-pain management
00:28:15 - Slop Code Bench and the maintainability oracle problem
00:37:59 - Why software factories aren't like car factories
00:42:49 - "There will always be alpha in reviewing something"Build your software factory, one workflow at a time, with Tessl:
https://tessl.co/4ep🔔 Subscribe for weekly episodes on AI-native development
Where do you land on the determinism-to-adaptability slider — plan tightly, or let the agent run? Tell us in the comments.
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AWS's Marc Brooker: Specs, Not Code, Are the Hard Part
16 september 2026 · 57 min
Spec-driven development is reshaping what software engineers actually do all day, and Marc Brooker, VP and Distinguished Engineer at AWS, has read 3,000 to 4,000 postmortems that convinced him the code was never the hard part. In this episode of The AI Native Dev, Marc explains why testing and specification are now the real engineering work, why metastable failures keep taking down systems that look perfectly healthy, and why he still won't let AI write a single word of his blog. Lees meer
What we cover:
– Why spec-driven development is turning testing into the hardest part of software
– What metastable failures are, and why they keep taking healthy-looking systems down
– How agentic coding tools can learn from postmortems and build their own memory
– Why classic authorization breaks down once you're writing agentic policy
– Why Marc won't let AI write his blog, but is fully comfortable with AI-generated code
– What it takes to bring junior engineers up to speed in an AI-native industryChapters:
00:00:00 - Introduction
00:02:17 - Marc Brooker: 18 years at AWS building agentic dev tools
00:04:36 - Inside Strands, AWS's open source agent SDK
00:06:44 - Fifteen years on call, and what agents still can't debug
00:11:23 - Metastable failures and the humility of 4,000 postmortems
00:14:32 - Teaching agents to learn from postmortems and build their own memory
00:20:14 - Why agentic policy needs more than classic authorization
00:32:14 - The agentic software development hypothesis: spec-driven development, oracles, and testing
00:39:56 - Why Marc won't let AI write his blog (but will let it write code)
00:46:37 - Advice for leveling up junior engineers in the AI eraBuild your software factory, one workflow at a time, with Tessl:
https://tessl.co/[SLUG]🔔 Subscribe for weekly episodes on AI-native development
Do you trust an agent to run its own on-call rotation yet? Tell us in the comments.
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You Don't Need Juniors to Code. Hire Them Anyway.
8 september 2026 · 52 min
Coding agent reliability isn't a model problem, it's a gates problem. Ran Aroussi, creator of yfinance and founder of Automaze, argues that an "agentic workflow" is a contradiction in terms, and that the only thing standing between you and full autonomy is deciding where a human still signs off. He also thinks you stopped needing junior developers for their coding skills about a year ago, and that hiring them anyway is the only way the industry gets its next generation of architects. Lees meer
What we cover:
– Why "agentic workflow" is a contradiction, and when agentic coding is the wrong tool for the job
– Where a human still has to sign off, and what coding agent reliability actually depends on
– What a software factory changed about delivery times, pricing and the client backlog
– Why you no longer need juniors for their coding skills, and why you should hire them anyway
– Teachable knowledge vs earned knowledge, and the experience agents cannot compress
– The law firm model: why dev agencies may end up looking more like Kirkland & Ellis than a SaaS startupChapters:
00:00:00 - Introduction
00:03:05 - yfinance, open source and 30 million downloads a month
00:06:03 - Automaze and the MUXI agent application server
00:10:42 - Where to draw the line on agent autonomy
00:15:12 - Software factories and fully autonomous merges
00:22:25 - The architect archetype and the four-phase ladder
00:28:11 - Teachable knowledge vs earned knowledge
00:30:14 - What agents did to delivery times and pricing
00:37:03 - Why "agentic workflow" is an oxymoron
00:41:08 - Anthropic, OpenClaw and the end of flat subscriptionsBuild your software factory, one workflow at a time, with Tessl:
https://tessl.co/vut🔔 Subscribe for weekly episodes on AI-native development
Where's your gate? Tell us in the comments where you still refuse to let a coding agent merge without a human looking.
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850 PRs a Week: How Tessl Runs a Software Factory
2 september 2026 · 51 min
The software factory that put up 850 pull requests in a week, with 85 to 90% of them handled by agents end to end. Tessl's Head of Product Dru Knox on how it actually got built, why it started with skills rather than automation, and the two things nobody expects a factory to change. Guy Podjarny digs into the skills-to-loops-to-factory path, and what breaks when you skip a step. Lees meer
What we cover:
– What a software factory actually is, and how it differs from a pile of disconnected automations
– Loop engineering: turning a skill into something that gets better every time it runs
– Why context engineering has to come before automation, and what stalls when it doesn't
– The two gains nobody forecasts from extra agent capacity: quality and fungibility
– Verifiers and evals, or how you enforce a standard that agents are free to ignore
– Context-driven code review, and why a general-purpose reviewer plateausChapters:
00:00:00 - Introduction
00:03:23 - Kikimora: Tessl's own dark factory
00:04:45 - Skills, loops and factories, defined
00:09:09 - 850 PRs a week, and the road to it
00:11:27 - The surprises: quality and fungibility
00:13:16 - Why a factory has to be context centric
00:16:40 - Context-driven code review and the wall teams hit
00:22:51 - Skills inventory across your whole code estate
00:28:38 - Verifiers, evals and enforcing what you wrote down
00:37:30 - Loops, automations and owning your software factoryBuild your software factory, one workflow at a time, with Tessl:
https://tessl.co/coi🔔 Subscribe for weekly episodes on AI-native development
If you've hit the plateau Dru describes, tell us where it stopped compounding for you.
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Lada Kesseler: I Trust AI Tests Less Than AI Code
25 augustus 2026 · 46 min
Lada Kesseler's approach to loop engineering is brutally simple: give the agent one step, make it write the result to a file, then read it back and go again. She calls it a centrifuge. Spin fast enough and the stupid comes out. Lees meer
The Logic20/20 principal engineer has spent the last year and a half building the ground rules, skills and refinement loops that fight the defaults coding agents ship with, starting with an instruction to stop trying to please her. Along the way she explains why she trusts AI-written tests less than AI-written code, why the description field in an agent skill was never meant for humans, and why most teams chasing a software factory are about to drown in garbage.
What we cover:
– Why agent ground rules have to give the model a mission to disagree with you
– The refinement loop that gets to quality when the first attempt never does
– Why an agent harness only works if it does one thing at a time
– Sketch prototypes: replacing your code with a markdown file and an agent
– Why AI-generated tests are more dangerous than AI-generated code
– Software factories, and what happens when you try to switch into one overnight*AI DevCon is coming back to New York this November*
Get 15% off your ticket with code POD15
https://tessl.io/devcon#ticketsChapters:
00:00:00 - Introduction
00:03:38 - Using AI for everything, not just code
00:06:15 - Ground rules: fixing the defaults agents ship with
00:10:02 - Agent skills and why the description isn't for humans
00:13:09 - Loop engineering: how you actually get to quality
00:16:03 - The biggest misconception about coding agents
00:20:43 - Sketch prototypes and the limits of AI architecture
00:27:48 - Reverse direction: you are the decider
00:31:58 - Why she trusts AI tests less than AI code
00:35:00 - Software factories, or drowning in garbageBuild your software factory, one workflow at a time, with Tessl:
https://tessl.co/k2t🔔 Subscribe for weekly episodes on AI-native development
Where are you on the …
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Every Repo Is a Software Factory Now | Don Syme, GitHub
18 augustus 2026 · 1 uur 5 min
Turning every repo into a software factory sounds like marketing until you see the machinery underneath. Don Syme, Principal Researcher at GitHub, breaks down continuous AI and GitHub Agentic Workflows, now in public preview. The short version: the agents are the easy part, the quality gates and guardrails are the work. Lees meer
Watch Don's AI DevCon Talk here: https://youtu.be/kbvqRWY-bUs
What we cover:
– What continuous AI is, and why it sits beside CI/CD rather than inside it
– Why bounding the context is what stops automated agents going off the rails
– Whether the repo is really the right unit for a software factory, and where monorepos break it
– One workflow or an agent zoo? Cost control, scheduling and model exams
– Quality gates, human review, and equipping the reviewer instead of drowning themChapters:
00:00:00 - Introduction
00:03:52 - What is continuous AI?
00:07:33 - From single player to multiplayer automation
00:09:42 - Bounding the context so agents don't go off the rails
00:12:57 - Software factories, loops and harnesses
00:18:44 - Inside GitHub Agentic Workflows
00:25:19 - Why the repo became the unit of production
00:37:11 - What belongs in the repo, and what doesn't
00:45:07 - One workflow or an agent zoo? Cost control in the factory
00:52:01 - Quality gates, human review and equipping the reviewer🌐 Tessl: https://tessl.io
🔔 Subscribe for weekly episodes on AI-native developmentIf you're building a software factory of your own, tell us in the comments where your bottleneck actually sits.
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The Background Check You Can't Run on an AI Agent
11 augustus 2026 · 48 min
The thing that makes an agent useful is the exact thing that makes it dangerous. Keycard co-founder Ian Livingstone breaks down why non-determinism is both the feature and the bug, and why identity, not model quality, is what really gates how much autonomy you can hand an agent. If you have ever clicked "always allow" without reading it, this one is about you. Lees meer
What we cover:
– Why authentication was enough in the cloud era and stops being enough with agents
– The background check you can't run on an agent, and what has to replace it
– Mission identity: who is acting, on whose behalf, and for what purpose
– Cross App Access, Agent Auth and the protocols trying to fix OAuth
– Consent fatigue, LLM as a judge, and where hard boundaries still belong
– Why MCP ships with an auth story and CLI tools don'tChapters:
00:00:00 - Introduction
00:03:43 - Why every wave of computing rewrites identity
00:04:50 - The feature and the bug are the same thing
00:10:23 - Why shared secrets break for agents
00:13:36 - The background check you can't run on an agent
00:19:42 - Chargebacks, delegation and proving intent
00:22:16 - Mission identity: a new layer
00:28:07 - Cross App Access, Agent Auth and emerging protocols
00:33:02 - MCP vs CLI, and how Keycard works
00:43:22 - Identity three years from now🌐 Tessl: https://tessl.io
🔔 Subscribe for weekly episodes on AI-native developmentWhere do you draw the hard line for your own agents? Tell us in the comments.
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Datadog Deleted All Its AI Context. It Worked.
4 augustus 2026 · 1 uur 1 min
Datadog's Language Foundations team deleted an entire folder of AI context files that had been carefully written and maintained for over a year, expecting a performance hit. Instead, their evals got better. Simon Boudrias, who runs Language Foundations at Datadog, walks Guy through what that taught his team about context rot, and the full journey of scaling AI coding agents to 4,000 engineers. Lees meer
What we cover:
– How Datadog scaled Cursor and Claude Code to 4,000 engineers in under a year
– Why Datadog deleted all of its AI context files, and what happened to eval scores
– Building an eval-driven code review system that replays old PRs to catch real incidents
– Where open-weight models like GLM 5.2 stand against the frontier
– Rethinking hiring and career ladders now that AI can run a real codebase interviewChapters:
00:00:00 - Introduction
00:03:19 - Simon's role and Datadog's 4,000-engineer org
00:04:31 - The Cursor rollout that took off overnight
00:07:53 - How Claude Code entered the picture
00:10:51 - Building dedicated Signals and Flows teams
00:11:58 - Why Datadog bet early on evals
00:33:44 - Deleting all their AI context and getting better evals
00:47:51 - Where open-weight models stand today
00:51:45 - Rethinking hiring and career ladders for AI
00:59:24 - The real prize: better decisions, not just productivity🌐 Tessl: https://tessl.io
🔔 Subscribe for weekly episodes on AI-native developmentWhat's the oldest file in your AGENTS.md or CLAUDE.md that you're afraid to delete? Tell us in the comments.
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Inside the Dark Factory: AI That Ships Code Solo
28 juli 2026 · 59 min
At Tessl, 95% of the code shipped by their internal "Dark Factory" has never been looked at by a human, and the team still ships hundreds of pull requests a week, including through entire weekends. Rob Willoughby, who leads AI engineering at Tessl, joins Simon to open up the hood on how it actually works: the orchestrator, the verification layers, and the failures that forced the team to rebuild trust from scratch. Lees meer
What we cover:
– How Tessl routes 65-70% of its own pull requests through an autonomous "Dark Factory"
– Why context in the repo matters more to output quality than which model you use
– How natural language "verifiers" turn code review taste into fast, cheap checks agents can pass or fail
– The queue bug that took dozens of pull requests to fix, and the from-scratch Elixir rewrite that stress-tested the whole system
– How to start building your own software factory, one verification layer at a timeChapters:
00:00:00 - Introduction
00:01:44 - Rob Willoughby joins: Tessl's PR numbers
00:04:01 - Live demo: kicking off two pull requests
00:14:03 - Building the Dark Factory: orchestrator vs. context
00:19:01 - Code review layers: Code Rabbit, Tessl Change Verify, and verifiers
00:29:58 - Earning trust: accountability in an autonomous system
00:33:20 - What broke: the queue bug and the Elixir rewrite experiment
00:39:56 - Onboarding new engineers into the factory
00:42:58 - Advice for teams starting their own software factory
00:52:03 - Back to the demo, and the road to 100% adoption🌐 Tessl: https://tessl.io
🔔 Subscribe for weekly episodes on AI-native developmentWhat would your own verification layer catch, and where would it break? Let us know in the comments.
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BONUS: Snyk Found Malware Inside AI Agent Skills
23 juli 2026 · 32 min
One Snyk developer's AI skill quietly handed their coding agent production credentials, and the security team found out the hard way. Krzysztof Huszcza, who leads AI security incubation at Snyk, joins this special Tessl and Snyk live stream to unpack the ToxicSkills research that uncovered 76 malicious agent skills in the wild, and what it actually takes to run coding agents safely at scale. Lees meer
What we cover:
– How Snyk's security team found 76 malicious skills hiding inside a popular open agent skill repository
– Why skills have become the go-to way developers hand context to their coding agents
– The internal incident at Snyk where a developer's skill exposed production credentials to an agent
– How the Tessl and Snyk integration scans every skill and MCP server for risk before you install it
– What's coming next with Snyk's new Evo product for governing coding agents at scaleChapters:
00:00:00 - Introduction
00:01:33 - Chris's role: AI security incubation at Snyk
00:02:16 - Snyk's roots as a developer-first security company
00:03:58 - New security challenges from AI coding agents
00:06:26 - Skills: the new way to give agents context
00:07:35 - Inside Snyk's ToxicSkills research: malware and prompt injection
00:11:35 - How developers can vet skills before installing them
00:14:38 - A real incident: exposed production credentials at Snyk
00:17:45 - Building a secure-by-default agent stack
00:23:35 - What's next: Snyk's new coding agent security product🌐 Tessl: https://tessl.io
🔔 Subscribe for weekly episodes on AI-native developmentWhat's the riskiest skill you've installed without checking it first? Let us know in the comments.
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From Living Room Hack to 30 AI Agents at Cyera
21 juli 2026 · 1 uur 6 min
An engineer builds an AI agent to manage his own life, decides an unrestricted "does everything" agent is too dangerous to trust, and ends up creating the internal agent platform that now runs 30 agents across his entire company. Lees meer
Ori Shoshan, tech lead at Cyera, walks through the guardrails, citation system, and knowledge graph that turned "let the agent do anything" into "let the agent do exactly what it's supposed to, and nothing else."
What we cover:
– Why one engineer built his own AI agent in his living room, and how it grew into Cyera's internal agent platform
– Whitelisting tools instead of blacklisting them, plus the "escape hatch" that keeps agents honest
– Backing every claim with a citation and using a second model to catch hallucinations before they reach a human
– Trading RAG for a knowledge graph the agent can walk like a wiki
– Turning "use this platform" into "build your own agent" to drive adoption across an entire engineering org
– Running agents on confidential data that can investigate everything but can only ever say what's been clearedChapters:
00:00:00 - Introduction
00:02:04 - Meet Ori Shoshan and what Cyera does
00:06:50 - The living-room spark: why Ori built his own agent
00:11:09 - Whitelisting tools instead of blacklisting them
00:12:55 - Why every claim needs a citation
00:15:35 - Reducing hallucinations with clean-context verification
00:21:04 - Taking Borg to Slack: the first agents at work
00:25:33 - Why naming your agent drives adoption
00:39:53 - Knowledge graphs over RAG
00:52:00 - The AI Captains: scaling adoption with carrots, not sticks🌐 Tessl: https://tessl.io
🔔 Subscribe for weekly episodes on AI-native developmentHave you built guardrails like this into your own agents? Let us know what's worked (or blown up) for you in the comments.
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Patrick Debois Maps the Patterns of AI-Native Dev
14 juli 2026 · 48 min
After DevOps, Patrick Debois has a new map. The godfather of DevOps returns to walk through his AI patterns research: how he tracks where AI-native development is heading by mining social signals instead of surveys, why the stack is settling into patterns worth learning, and why your AI budget problem is really an optimization problem. Lees meer
What we cover:
– Inside the AI patterns site at Tessl.io/patterns and how it's auto-generated
– The four layers of AI enablement: agent, team, platform, and organization
– Solo, shared, and multiplayer modes: the compounding effect of shared context
– Managing agent costs: budgets as a forcing function for optimization
– Scaling adoption: find the success story, don't fight the skeptics
– Hiring system thinkers instead of elegant codersChapters:
00:00:00 - Introduction
00:02:28 - Why AI grips Patrick Debois
00:04:33 - Agent, team, platform and org enablement
00:06:36 - Is AI-native development maturing?
00:11:19 - Inside the AI patterns site
00:18:14 - Quality, security and the IDE as review interface
00:22:45 - Changing roles and harness engineering
00:26:32 - Hiring in the AI era
00:30:31 - Scaling adoption across the org
00:42:03 - Cost management and ROI🌐 Tessl: https://tessl.io
🔔 Subscribe for weekly episodes on AI-native developmentWhere is your organization on the maturity curve? Drop your take in the comments.
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Inside Anthropic: How Claude Tag Is Changing Agentic Work
7 juli 2026 · 58 min
Six people reacted to Boris's side-project Slack post. A year later, Claude Code is ubiquitous, and the company just launched its next evolution: Claude Tag, an AI teammate that lives in Slack. Lees meer
Lamis Mukta, Member of Technical Staff at Anthropic, joins Simon Maple to unpack how Claude Tag works, why Anthropic built it, and what it took internally to go from a scrappy side project to a company-wide habit.
What we cover:
– What Claude Tag actually is, and how it differs from Claude Code and Cowork
– Why trust in AI agents is really a function of model capability, not just comfort
– The internal "dogfooding" culture that shaped Claude Code and Claude Tag
– How Anthropic secures multiplayer AI with agent identities and channel-level permissioning
– Where Claude and Claude Tag show up outside of engineering at Anthropic
– Dreaming: how Anthropic's managed agents continually improve their own memoryWatch Lamis' talk from AI Native DevCon London 2026 here: https://youtu.be/tTcxVv8HHNw
Chapters:
00:00:00 - Introduction
00:01:46 - Meet Lamis Mukta from Anthropic
00:02:56 - What is Claude Tag?
00:10:07 - From single-player to multiplayer agentic coding
00:15:50 - Trust, capability, and the METR chart
00:21:02 - How the industry is really using Claude Code
00:26:33 - The Claude Code origin story
00:33:47 - Agent identities and permissioning at scale
00:40:23 - Claude beyond engineering at Anthropic
00:48:16 - Dreaming and practical tips for rolling out Claude Tag🌐 Tessl: https://tessl.io
🔔 Subscribe for weekly episodes on AI-native developmentWhat's the one thing your team would delegate to an AI teammate first? Let us know in the comments.
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The Tessl Agent: Build Your Software Factory on Autopilot
30 juni 2026 · 53 min
What if the whole point of your AI agent was to eventually make itself redundant? Dru Knox, Head of Product at Tessl, introduces the Tessl agent — a new interface built not just for AI-assisted coding, but for building the software factory that keeps improving without constant human input. Lees meer
This is a conversation about loop engineering: how to set up automated feedback cycles so your agents get smarter, your code review gets tighter, and your team ships more without adding more toil.
What we cover:
– What the Tessl agent is and how it fits into the broader Tessl platform
– Loop engineering: why building automated feedback loops is the right place to start
– How the agent sets up and continuously improves agentic code review
– Why optimising your AI agent costs is usually the wrong lever to pull
– The case for open, modular software factories — and the risk of vendor lock-in
– UX expectations in the AI era: why outcome-oriented interfaces are now the baseline🌐 Tessl: https://tessl.io
🔔 Subscribe for weekly episodes on AI-native developmentWhat's your approach to building feedback loops into your agent workflows? Drop a comment — we'd love to hear how teams are thinking about this.
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Why Agents Are Forcing Enterprises to Finally Fix Their Dev Process
25 juni 2026 · 1 uur
Enterprises are finally being forced to care about their software development lifecycle — not because anyone suddenly got disciplined, but because agents cost money and the waste is now visible. When it was humans, it was "Timmy's just lazy." Now it's a line item. Lees meer
Simon Maple sat down with Patrick Debois (the godfather of DevOps, now DevRel at Tessl), Tammuz Dubnov (co-founder and CEO of Autonomy AI), and Daniel Jones (Head of Product at re:cinq) at AI Native DevCon London for a wide-ranging panel on AI enablement — who owns it, what's breaking, and what the organisations getting it right are actually doing differently.
What we cover:
– Who should own agentic coding adoption inside an enterprise, and why platform teams are already filling the vacuum
– The "Timmy's lazy" problem: why agent cost visibility is forcing process discipline that humans never got
– Why PR-based workflows are an anti-pattern inside enterprises once you're moving at agent speed
– The PUMP framework (Plan, merge, polish): how one team is shipping features with developers, PMs, and designers all opening PRs –
Rethinking what a "test" is in an agentic world — and why feedback loops matter more than first-pass correctness
– The biggest mistake enterprises are making right now: piecemeal adoption with no mandate and no shared tooling
🌐 Tessl: https://tessl.io
🔔 Subscribe for weekly episodes on AI-native development
What's your team's approach to AI enablement — central mandate or letting individuals find their own way? Drop it in the comments.
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BONUS: DevCon London: Real Talk on AI ROI, Harnesses & Evals
23 juni 2026 · 24 min
From the expo floor of AI Native DevCon London, Simon Maple went straight to the developers — speakers, attendees, and sponsors — to ask what's actually working with AI in 2026. The verdict? Outcomes beat outputs every time, 4,000-hour workloads are collapsing to 20 minutes, and the real bottleneck isn't code. Lees meer
This is a conference floor walkthrough: honest, unscripted takes on harness engineering, evals, AI adoption mindsets, and the change management challenge that nobody talks about enough.
What we cover:
– Why measuring token usage, code commits, and outputs will lead your team astray
– How NearForm cut a 4,000-hour AML backlog to 20 minutes using agents
– Harness engineering and evals as the developer skills that matter most in 2026
– Why change management — not tooling — is the missing ingredient for real AI ROI
– How AutonomyAI is letting PMs and designers ship directly to productionChapters:
00:00:00 - Welcome to AI Native DevCon London
00:01:03 - Chris Baty: Outcomes Over Outputs
00:05:15 - Martin: How Tooling Changed Everything
00:07:21 - Ryan: Harnesses, Evals and Skills
00:08:50 - Manny Saka: Plan Before You Prompt
00:11:58 - Cian O Maidin, NearForm: Real AI ROI
00:15:21 - Snyk: Building Trust at Scale
00:17:01 - AutonomyAI: Shipping Without Engineering
00:21:35 - Tessl Agent: Harness Engineering
00:22:41 - Closing Thoughts🌐 Tessl: https://tessl.io
🔔 Subscribe for weekly episodes on AI-native developmentWhether you're cautious or YOLOing it with AI — drop where you land in the comments.
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AI Security & the Agent-Ready Web: Experts Weigh In
16 juni 2026 · 1 uur 3 min
What does it mean to build securely when agents can negotiate their own guardrails? And what happens to the web — CLIs, frameworks, even the browser itself — when the primary user is no longer human? At AI Native DevCon London, Simon Maple sat down with two panels of experts to find out. Lees meer
First: a security roundtable with Joseph Katsioloudes from GitHub, Liran Tal from Snyk, and John Groetzinger from Cisco.
Then: a web AI conversation with Dana Lawson from Netlify, Maximiliano Firtman from codemia, and James Moss from Tessl.
What we cover:
– Why 83% of enterprises plan to deploy AI but only 29% feel ready to do so securely
– Prompt injection as a risk you have to accept — and how least privilege and sandboxing are your real defences
– The "agent experience" concept: why systems built for human eyes fail at machine scale
– Whether fundamentals like HTTP, semantic HTML, and accessibility still matter when agents do the heavy lifting
– How WebMCP lets websites expose tools directly to agents
— and why blocking them is like trying to turn off the sun
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Ryan Lopopolo: OpenAI's Framework for Shipping Code at 70 PRs/Week
9 juni 2026 · 55 min
Most engineering teams are still arguing about whether to use AI coding agents. Lees meer
Ryan Lopopolo's team at OpenAI shipped an entire product with no human-written code — and onboarding a new engineer made the team faster within two weeks.
That outcome didn't come from better prompts. It came from what Ryan calls Harness Engineering: the systems, constraints, and feedback loops that sit around the agent — the context it sees, the tools it can call, the tests and linters that close the loop, and the asynchronous CI jobs that catch slop before it compounds.
We sat down with Ryan at AI Native DevCon London 2026, and he got into the specifics: how his team went from 3.5 PRs per engineer per week to 70, why he inverts spec-driven development (build the code first, distill the spec second), and what he means when he says it's "borderline negligent" not to use a billion tokens a day.
It's one of the most grounded, production-focused conversations we've had on The AI Native Dev. The trailer is live now — and the full episode drops this week.
#HarnessEngineering #AINativeDev #SoftwareEngineering
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Why Developers Hit a Wall at 4 AI Agents
2 juni 2026 · 48 min
Engineering teams are shipping twice as many pull requests with AI — but merge rates on AI-generated PRs have dropped from 80% to 60%. Lees meer
Nick Arcolano, Head of AI & Research at Jellyfish, sits on one of the most comprehensive datasets in the industry: 250,000 developers, 40 million data points, monthly benchmarks on real agentic coding adoption across enterprise companies. What he's seeing in that data is both more promising and more complicated than the headlines suggest.
What we cover:
Why experienced engineers hit a hard ceiling at 4 concurrent agents, and what it would take to break through it
The 80/20 vs 60/40 merge rate gap between human and AI-generated pull requests — and what's actually causing it
How AI adoption reached 71% weekly active usage across 250K developers, and what "depth of use" really means
Why 2026 is the year the CFO gets involved — and how engineering leaders should prepare to show their receipts
The biggest misconception engineering leads have about what it takes to get to true AI-native development
Why companies have jet engines but are still building cars, and what the real architectural changes look like
Links:
🌐 Tessl: https://tessl.io
🔔 Subscribe for weekly episodes on AI-native development
If you're an engineering leader trying to make sense of the gap between the AI hype and what's actually showing up in production, drop your take in the comments.
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Don't Secure the Code. Secure the Coder.
26 mei 2026 · 40 min
AI agents don't just write insecure code — they can escape their sandboxes, delete files, and do whatever it takes to complete a task. The security mental model that served us through the cloud era isn't enough anymore. Guy Podjarny, founder of Snyk and CEO of Tessl, made the case at London's AI Security Summit: it's time to stop securing the code and start securing the coder. Lees meer
Recorded live at the AI Security Summit in London, this episode features conversations with Brian Vermeer (Snyk), Sam Stepanyan (OWASP London), and a full recording of Guy's keynote on why agentic development demands a fundamentally different approach to security.
What we cover:
Why shadow AI is the new shadow IT — and why CISOs can't secure what they can't see
Skills as a new supply chain attack surface (malicious, vulnerable, and negligent skills)
Why more context is not always better — and what the data says about focused skill design
The OWASP Top Ten for Agentic AI and what it means for teams building today
Why security must become agentic to keep up with the attackers who already are
The Context Development Lifecycle (CDLC) and how leading orgs are using it
Links: 🌐 Tessl: https://tessl.io
Subscribe for weekly episodes on AI-native development
What's the biggest security risk your team isn't talking about when it comes to agentic development? Drop it in the comments.
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The Hidden Security Risks of AI Coding Agents
19 mei 2026 · 41 min
Your AI coding agent has access to your secrets, pulls in content from the outside world, and can run shell commands. According to Joe Holdcroft, that combination makes you one prompt injection away from a very bad time. The tools haven't changed the fundamentals of security — they've just made every existing risk move faster, and introduced a few genuinely new ones. What we cover: Lees meer
Why the "lethal trifecta" of agent capabilities creates a novel threat surface
How text and markdown files have become a new class of vulnerability
Slop squatting: the attack vector created by agents hallucinating package names
The context supply chain — and why it mirrors the early days of npm security
What a "CBOM" (context bill of materials) might look like and why we may need one
How to think about agent trust using the contractor mental model
Chapters:
00:00 Introduction
01:40 The Lethal Trifecta: why agents are inherently risky
03:23 Same hygiene, higher stakes
04:08 Text as a vulnerability: markdown as a security risk
06:08 Do AI tools make you more or less secure?
08:09 Snyk + Tessl: scanning skills in the registry
10:10 The context supply chain problem
14:28 The CBOM: do we need a context bill of materials?
17:35 Secrets, credentials, and principle of least privilege
22:25 Balancing security with developer velocity
36:54 One piece of advice for CTOs going all-in on AI
Links:
🌐 Tessl: https://tessl.io
Subscribe for weekly episodes on AI-native development
If you're thinking about AI governance in your team, drop a comment — how are you handling context supply chain today?
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"AI Doesn't Stand for Artificial Intelligence" — Venkat Subramaniam's Take Will Change How You Think About It
12 mei 2026 · 53 min
Is AI actually intelligent — or just very fast at guessing based on bad data? Lees meer
Venkat Subramaniam, 40-year programming veteran, educator, and co-founder of Arc of AI, joins the AI Native Dev Podcast to share a perspective that cuts through the hype: AI stands for Accelerated Inference — not Artificial Intelligence. And that reframe changes everything about how developers should use it.
In this episode, Venkat unpacks why the speed of AI generation has outpaced our ability to review it, why you can delegate work to AI but never your reputation, and tells the story of a $2 million feature that got cancelled with one simple question: "Why are we building this?"
🔑 Topics covered:
— Why "Accelerated Inference" is a more honest name for AI
— The code quality crisis AI has inherited from decades of bad human code
— Accountability, lawsuits, and professional responsibility in the AI era
— What makes a senior developer
— and why AI widens that gap
— The one question that should precede every feature decision
— Why vibe coding is the most overrated AI trend right now
🎙️ Guest: Venkat Subramaniam — author, educator, and co-creator of Arc of AI
🎙️ Host: Simon Maple
📅 Recorded live at Arc of AI, Austin
🔔 Subscribe for weekly conversations with practitioners (not vendors) building with AI.
🎧 Listen on Spotify / Apple Podcasts — search "AI Native Dev"
🌐 Community & events: tessl.io/community
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The Creator of Spring Thinks You Can't Code Serious Software With AI
5 mei 2026 · 57 min
Rod Johnson — the creator of Spring Framework and founder of Embabel — joins Simon Maple on the AI Native Dev Podcast to share his unfiltered take on where enterprise AI is actually heading. Lees meer
In this episode, Rod breaks down why enterprises are making a huge mistake rewriting Java apps in Python, why vibe coding will destroy your codebase if left unchecked, and why this might be the last generation of frameworks that developers ever choose for themselves.
Rod also pulls back the curtain on Embabel — the new JVM-native agentic framework he's building — including how it borrows its planning algorithm from NPC AI in video games, why he's skeptical of MCP despite its hype, and the AI failure pattern he keeps seeing in large enterprises.
Whether you're a Java developer navigating the AI wave or a tech lead trying to figure out where to actually invest, this is essential listening.
Topics covered:
0:00 Intro — Rod Johnson, Spring creator, returns to building
5:16 PhD in 19th century Parisian piano music (yes, really)
6:15 What made Rod come back and build Embabel
7:36 "The universe would end before Python could execute inference"
13:08 The #1 enterprise AI failure pattern Rod keeps seeing
16:23 "You cannot vibe code serious software"
19:37 Why Embabel uses a video game NPC planning algorithm
27:47 Why Rod is an MCP skeptic
53:54 "This is the last wave of frameworks chosen by people"Links:
🔗 Embabel: https://embabel.com🎟 AI Native Dev London (June 1–2): https://ainativedev.io — use code POD30 for 30% off.
Subscribe for weekly conversations with the engineers and founders building the future of AI-native development.
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What OpenAI, Stripe & ElevenLabs Devs Do Differently Now | AI Native Dev
28 april 2026 · 1 uur 6 min
How aligned are teams at Google DeepMind, OpenAI, Stripe, and ElevenLabs on what’s changing in software development? Lees meer
At AI Engineer London, with 100+ speakers and 1000+ engineers in the room, Simon Maple pulls together perspectives from across the ecosystem to understand where AI-native development is heading.
• why traditional CI/CD “is dead”
• the growing need for automated code review and guardrails
• the move from more context is better to right context at the right time
• the difference between general-purpose models vs specialized domain modelsTo catch conversations like these in person, register for AI DevCon in London on 1st and 2nd June 2026.
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Logan Kilpatrick on Who Ships AGI, DeepMind and the Problem With More Software
21 april 2026 · 38 min
"If you could have a system that could build anything with code, humans can't compete on the same level. That's narrow superintelligence, and we're close." Lees meer
In this episode of AI Native Dev, Simon Maple sits down with Logan Kilpatrick, who spent years at OpenAI working alongside Sam Altman before moving to Google DeepMind as Group Product Manager.
They get into:
There will be 100x more developers in the world because of AI
AGI will be a product, not a model
The way you used AI tools three months ago is already wrong
What's actually changing inside the Gemini team and why it matters for developers building with it today
The developers who win won't be the ones who mastered today's tools. They'll be the ones who never stopped learning the new ones.
Building AI-native? AI DevCon is coming to London, 1st and 2nd June 2026: https://tessl.io/devcon/
Connect with us here:
Simon Maple: https://www.linkedin.com/in/simonmaple/
Logan Kilpatrick: https://www.linkedin.com/in/logankilpatrick/
Google DeepMind: https://www.linkedin.com/company/googledeepmind/
Tessl: https://www.linkedin.com/company/tesslio/?originalSubdomain=uk
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Everything 100 Episodes Revealed About AI Native Dev
14 april 2026 · 57 min
When did writing code stop being the job and start being the hobby? Lees meer
One hundred episodes in, Guy Podjarny and Simon Maple pull the clips, check the predictions, and trace the through line across conversations with guests from Datadog, ElevenLabs, GitHub, and more.
They get into:
The move from spec-driven to context-driven development
Why humans become the bottleneck in code review
What changes when agents run the SDLC end-to-end
Adoption across orgs vs depth of actual usage
Thanks to every guest and every listener who made this possible. On to the next hundred.
Want to have these conversations in person? AI DevCon is coming to London on 1st and 2nd June, 2026.
Book your tickets here - https://tessl.io/devcon/
Connect with us here:
Guy Podjarny- https://www.linkedin.com/in/guypo/
Simon Maple- https://www.linkedin.com/in/simonmaple/
Tessl- https://www.linkedin.com/company/tesslio/
AI Native Dev- https://www.linkedin.com/showcase/ai-native-dev/
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How DeepSeek leveraged Qwen and Llama to build its model in $5M
7 april 2026 · 40 min
Meta’s Llama might not actually be open source AI, and the developers building on it have no idea. Lees meer
In this episode of AI Native Dev, Simon Maple sits down with Amanda Brock, CEO of OpenUK, to break down what open source actually means in the age of AI and why most of the industry is getting it wrong.
They get into:
what open washing is and why it is happening across major models right now
how DeepSeek built a frontier model for $5 million instead of $100 million
why Chinese developers are already on Kimi and the west is sleeping on it
what the west needs to learn from China's open source strategy before it is too late
Eight companies already control the world's digital infrastructure. We cannot let that happen again with AI.
Connect with us here:
Simon Maple: https://www.linkedin.com/in/simonmaple/
Amanda Brock: https://www.linkedin.com/in/amandabrocktech/
OpenUK: https://www.linkedin.com/company/openuktechnology/?originalSubdomain=uk
Tessl: https://www.linkedin.com/company/tesslio/
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Why Every Developer needs to know about WebMCP Now
31 maart 2026 · 1 uur
An agent cannot read your website. And that needs to change. Lees meer
In this episode of AI Native Dev, Guy Podjarny sits down with Maximiliano Firtman, 30-year web developer and author of 14 books, to talk about what building for the web looks like when traffic comes from agents and humans both.
They get into:
why AI agents taking screenshots of your website is inefficient and expensive
what Web MCP is and how it gives agents a direct API into your website
how a 200MB Apple model running offline is opening up a whole new category of web apps
why every vibe coded app is a web app and what that means for the future of the web
Your next visitor might not be human. Are you ready for that?
Connect with us here:
Maximiliano Firtman: https://www.linkedin.com/in/firtman/?locale=es
Guy Podjarny: http://uk.linkedin.com/in/guypo
Tessl: https://www.linkedin.com/company/tesslio/
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Stop Maintaining Your Code. Start Replacing It
24 maart 2026 · 1 uur 2 min
"The code that we have is a liability. The system is the asset we're building." Lees meer
Chad Fowler, VC at Blue Yard Capital and former CTO at Wunderlist, sits down with Guy Podjarny to discuss the Phoenix Architecture: software designed to be replaced rather than maintained.
In this episode:
• why was the code written by Chad never longer than a page
• how he replaced 70% of a codebase in 3 months and cut costs by 75%
• shipping AI code no human ever reviewed, and how to make it safe
• the shadow specs your agents are making without you
• why your system should work with the worst LLM, not just the bestIf you're still thinking about your codebase the old way, this one will change that.
Connect with us here:
Chad Fowler: https://www.linkedin.com/in/fowlerchad/
Blue Yard Capital: https://www.linkedin.com/company/blueyard-capital/
Guy Podjarny: https://uk.linkedin.com/in/guypo
Tessl: https://www.linkedin.com/company/tesslio/
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We Scanned 3,984 Skills — 1 in 7 Can Hack Your Machine
17 maart 2026 · 35 min
Most developers install skills without reading what's inside them. But that's exactly what attackers are counting on. Lees meer
Simon Maple sits down with Brian Vermeer from Snyk at DevNexus to get into the security risk hiding inside the skills and MCPs running on your local machine. They scanned over 4,000 skills and found that 1 in 7 had at least one critical security vulnerability.
Here’s what you need to know:
Why prompting your agent to write secure code doesn't make it secure
How a trusted skill can update silently and start offloading your credentials
What prompt injection actually looks like inside a skill file
Why vibe coding makes the attack surface bigger, not smaller
How the Snyk agent scan catches what you'd never spot manually
Every skill on the Tessl registry now has a Snyk security scan attached. Check before you install.
Connect with us here:
Simon Maple: https://www.linkedin.com/in/simonmaple/
Brian Vermeer: https://www.linkedin.com/in/brianvermeer/
Snyk: https://www.linkedin.com/company/snyk/
Tessl: https://www.linkedin.com/company/tesslio/ -
The Greatest Time to Build a Startup (The AI-Native Advantage)
10 maart 2026 · 1 uur 1 min
The best agentic developers throw away their agent's work without guilt, run three agents at once and only use one, and treat their AI like a junior developer they genuinely dislike. It sounds wrong. It works. Lees meer
Daniel Jones, Head of Product at re:cinq, has upskilled hundreds of developers across Northern Europe's largest enterprises. In this episode he joins Simon Maple to share the counterintuitive habits, hard data, and practical frameworks behind high-performing agentic development teams.
On the docket:
Why bad engineering practices get worse, not better, with AI agents
The exact conditions that make your agent hallucinate every time
Why your AGENTS.md is quietly working against you
How to manage context before it kills your productivity
What enterprise AI rollout actually looks like at scale
Why the worst managers get the most out of agentic coding
If your team is adding AI and wondering why things aren't getting faster, this episode is for you.
Connect with us here:
Daniel Jones: https://www.linkedin.com/in/danieljoneseb/
Simon Maple: https://www.linkedin.com/in/simonmaple/
Tessl: https://www.linkedin.com/company/tesslio/ -
Why Your Agent Needs Memory, Not Just Context
3 maart 2026 · 44 min
Not onboarding your agent is on you. Lees meer
Richmond Alake, Director of AI Developer Experience at Oracle, joins Simon Maple to make the case that most agent failures come down to one thing: memory. Not the model, not the infrastructure. Memory.
On the docket:
why skills are just SOPs your organisation already has written down
the job title that is replacing prompt engineers
file systems vs databases for agent memory (and why one gets you hacked)
the memory trick that makes agents feel actually intelligent
why the agent loop and training loop are about to become one
The developers who figure this out first are going to be very hard to compete with.
Connect with us here:
Richmond Alake: https://www.linkedin.com/in/richmondalake/
Simon Maple: https://www.linkedin.com/in/simonmaple/
Tessl: https://www.linkedin.com/company/tesslio/
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Cisco Principal Engineer's Fix for AI Code Security
25 februari 2026 · 33 min
Your AI coding agent learned from millions of lines of code, including insecure ones. That means by default, it can write vulnerable code too. Lees meer
So how do you fix that?
John Groetzinger, Principal Engineer at Cisco, built CodeGuard, a security skills layer that teaches coding agents how to write and review code securely. He tested it against real scenarios.
The result:
84% success rate vs 47% baseline. Nearly 2× improvement.In this episode we get into:
how CodeGuard works
why Cisco open sourced it
the surprisingly simple method that gets agents to fix their own mistakes
Try CodeGuard: cisco/software-security on the Tessl registry.
Connect with us here:
John Groetzinger: https://www.linkedin.com/in/john-groetzinger/
Cisco: https://www.linkedin.com/company/cisco/
Simon Maple: https://www.linkedin.com/in/simonmaple/
Tessl: https://www.linkedin.com/company/tesslio/ -
Why Context Beats Every Prompt You'll Ever Write
17 februari 2026 · 31 min
Most teams think agentic dev is about writing better prompts. It's not. Lees meer
Guy Podjarny and Simon Maple explain why managing context, not crafting prompts, is what separates teams that scale with agents from teams that don't. They walk through a practical framework for building, evaluating, and distributing the context your agents actually need.
In this episode:
• Why agents fail without structured context about your internal platform
• The 3 context layers: policies, platform docs, and application context
• How to build regression evals and torture tests for your agents
• The Context Development Lifecycle (CDLC) - a new loop for agentic devYour agents are only as good as the context you give them.
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From IBM Acquisition to AI-Native Observability | Dash0 CEO
10 februari 2026 · 56 min
"Charts are good for users, not good for agents. Agents look at the underlying data and do deep analysis." Lees meer
Mirko Novakovic built Instana, sold it to IBM, and now he's building Dash0, rethinking observability for agents, not humans.
In conversation with Guy Podjarny, he explains:
• why OpenTelemetry turned out to be perfect for AI
• how UX changes when agents are your primary users
• why interactive collaboration beats static chat outputs
• the survival question for observability vendors in the AI eraOnly 2-3 people in most companies can truly debug production. That knowledge lives in their heads and disappears when they leave. Mirko's betting agents will change that.
Connect with us here:
Mirko Novakovic: https://www.linkedin.com/in/mirkonovakovic/
Dash0: https://www.linkedin.com/company/dash0hq/
Guy Podjarny: https://www.linkedin.com/in/guypo/
Tessl: https://www.linkedin.com/company/tesslio/ -
The End of Fragmented Agent Context
3 februari 2026 · 44 min
One skill took coding success from 28% to 71%. Another made things worse. Lees meer
Guy Podjarny and Simon Maple tested 1000+ agent skills and reveal which ones actually work, which hurt performance, and why anecdotal evidence isn't enough anymore.
Tessl Skills Registry is the first package manager for agent skills with built-in evaluations, versioning, and lifecycle management. Explore tested skills and see real performance data: https://tessl.io/registry
On the docket:
• Claude roasted Anthropic's own skill with a 27% score ("monolithic wall of text")
• Why some popular skills actually decrease agent performance
• How Tessl is bringing package managers and evals to agent skillsWhether you're creating your first skill or managing them across your dev team, this is your roadmap for making agent skills actually work.
Connect with us here:
1. Guy Podjarny - https://www.linkedin.com/in/guypo/?originalSubdomain=uk
2. Simon Maple - https://www.linkedin.com/in/simonmaple/
3. Tessl - https://www.linkedin.com/company/tesslio/
4. AI Native Dev - https://www.linkedin.com/showcase/ai-native-dev/ -
The Developer Skills That Will Actually Survive AI
27 januari 2026 · 56 min
“You have to prioritize between the thing you want to do and the thing that actually is driving the business, that’s what really big companies are fighting every single day.” Lees meer
As former CEO of GitHub and now a startup founder again, Thomas Dohmke brings a rare, inside-out perspective on innovation across both worlds.
In conversation with Guy Podjarny, he explains:
• why startups and incumbents fight in different weight classes
• why humans shouldn’t sit in every feedback loop as agents scale
• the importance of learning as a core skill for future developersAI Native Dev, powered by Tessl and our global dev community, is your go-to podcast for solutions in software development in the age of AI. Tune in as we engage with engineers, founders, and open-source innovators to talk all things AI, security, and development.
Connect with us here:
1. Thomas Dohmke- https://www.linkedin.com/in/ashtom/
2. Guy Podjarny- https://www.linkedin.com/in/guypo/
3. Tessl- https://www.linkedin.com/company/tesslio/
4. AI Native Dev- https://www.linkedin.com/showcase/ai-native-dev/ -
How Too Much Information Destroys Agent Performance
20 januari 2026 · 20 min
Most AI agents fail because you're using them wrong. Here’s what actually works in production. Lees meer
In this episode, Simon Maple sits down with Itamar Friedman (CEO of Qodo) and Robert Brennan (CEO of OpenHands) at QCon AI. They pull back the curtain on why agents hallucinate, provide inconsistent answers, and ship low-quality code.
On the docket:
• Why a third of developer-reported AI output is incorrect
• Why you must separate creative coding agents from structured review agents
• How excessive information destroys agent performance
• Parallelizing agents across 1,000+ repositories to resolve CVEs and legacy debtRecorded live at QCon, this one's straight from architects building the future of the AI-native dev cycle.
Connect with us here:
Itamar Friedman: https://www.linkedin.com/in/itamarf/
Robert Brennan: https://www.linkedin.com/in/robert-a-brennan/
Simon Maple: https://www.linkedin.com/in/simonmaple/
Tessl: https://www.linkedin.com/company/tesslio/
AI Native Dev: https://www.linkedin.com/showcase/ai-native-dev/Check out the Qcon Batch one: https://youtu.be/OnM7sWLnAfw?si=mxtM_jdQJ_EdpLXt
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Intelligence ≠ Knowledge: Why Context Beats Bigger Models
13 januari 2026 · 1 uur 12 min
In this special milestone episode, Simon Maple and Guy Podjarny celebrate 1 million views by looking back at the chaos of 2025 and forecasting the high-stakes reality of 2026. Lees meer
On the docket:
• Why appearing on this show has become a leading indicator for getting acquired or raising billions (and whether Simon should start charging a 2% carry).
• The end of prompt engineering and the rise of context as the ultimate competitive advantage.
• Our boldest 2026 predictions, including open models taking enterprise share and AI coding going truly multiplayer.Whether you're building a solo unicorn or leading an enterprise team, this is your roadmap for staying ahead of the 2026 AI curve.
Check out the discussed EPs:
Olivier Pomel: https://youtu.be/-5N53Xq5DX4?si=f8hFrjtO4eZQ4sYJ
Macey Baker: https://youtu.be/ENL9WJ93fVU?si=ffDkY4OYxVxKlCOc
Maksim Shaposhnikov: https://youtu.be/BQbuAykmleQ?si=u_dDLkNKNgCq10ax
Yaniv Aknin: https://youtu.be/WjMy0JFTQZ0?si=yoFbEvoWhsIicetw
Merrill Lutsky: https://youtu.be/kH_Z_k6hIwg?si=-4R1Yv45pRO2LcKq
Samuel Messing (Slack): https://youtu.be/jCVvVVXcxbQ?si=o004uK9g6xIKxiMp
Maor Shlomo (Base44): https://youtu.be/TKrXyZr6UgM?si=c-WXIzqDHbl9bW4d
Quinn Slack (Sourcegraph): https://youtu.be/ZTLSMN8eISs?si=1WtChQh6UTvoFcbJ
Mati Staniszewski: https://youtu.be/ImknDhWdDkQ?si=3C-3vWpvou-0pAuU
Victor Riparbelli: https://youtu.be/5OS3pZ3yXrg?si=AB8v10U80zyUpv3pResources:
Cursor acquiring Graphite: https://graphite.com/blog/graphite-joins-cursor
Wix acquiring Base44: https://techcrunch.com/2025/06/18/6-month-old-solo-owned-vibe-coder-base44-sells-to-wix-for-80m-cash/
Amp: https://ampcode.com/free
Kilo Code: https://kilo.ai/
Groq: https://groq.com/newsroom/groq-and-nvidia-enter-non-exclusive-inference-technology-licensing-agreement-to-accelerate-ai-inference-at-global-scale -
What AI Engineering Looks Like at Meta, Coinbase, ServiceTitan and ThoughtWorks
6 januari 2026 · 1 uur 13 min
What does it take to make AI work inside engineering teams? Lees meer
This high-stakes compilation episode with Ian Thomas (Meta), Wesley Reisz (ThoughtWorks), Sepehr Khosravi (Coinbase), and David Stein (ServiceTitan) goes inside the engineering rooms of the world's most sophisticated tech organisations to uncover how they're moving past AI hype into AI-native production.
On the docket:
• How Meta achieved 80% weekly AI adoption through grassroots community building instead of top-down mandates
• Why ThoughtWorks uses the RIPPER-5 framework to structure AI workflows and prevent agents from jumping straight to code
• When Coinbase engineers use Cursor versus Claude Code and why sub-agents are critical for managing context without bloat
• How ServiceTitan migrated 247 legacy metrics in three weeks using self-healing validation loops and standardized agent tasksIf you're trying to make AI useful inside your team, this episode shows exactly what matters.
Connect with us here:
Ian Thomas: https://www.linkedin.com/in/anatomic/?originalSubdomain=uk
Wesley Reisz: https://www.linkedin.com/in/wesreisz/
Sepehr Khosravi: https://www.linkedin.com/in/sepehrkhosravi/
David Stein: https://www.linkedin.com/in/steindavidj/
Simon Maple: https://www.linkedin.com/in/simonmaple/
Tessl: https://www.linkedin.com/company/tesslio/
AI Native Dev: https://www.linkedin.com/showcase/ai-native-dev/Resources:
AI Scouts - https://aiscouts.org/
Thoughtworks’ knowledge base insights - https://www.thoughtworks.com/en-sg/insights/blog/generative-ai/deep-kernel-level-AI-RCA-with-MCP
Check out Birgitta Böckeler’s full EP - https://youtu.be/uYmSco0s6Xo?si=FIK293aJXu3sxvt6
Cursor’s community forum - https://forum.cursor.com/t/i-created-an-amazing-mode-called-riper-5-mode-fixes-claude-3-7-drastically/ -
What Developers Need To Know About Agents Before 2026
30 december 2025 · 47 min
2025 changed what it means to be a developer. Lees meer
And 2026 is about to change even more.
This Tessl episode brings a year-end reflection on how agents reshaped software development and what developers need to unlearn next, featuring Reuven Cohen, Founder of the Agentic Foundation, Maor Shlomo, Founder of Base44, and Maksim Shaposhnikov, Technical Member at Tessl.
On the docket:
• Reuven Cohen on why most agent systems fail and what agentic engineering demands
• Maor Shlomo on where vibe coding works, where it breaks, and how guardrails protect long-term software
• Maksim Shaposhnikov on developer autonomy, IDE vs terminal agents, and designing for delegationThis episode looks ahead to 2026 and examines the systems developers will be responsible for next.
Connect with us here:
1. Reuven Cohen: https://www.linkedin.com/in/reuvencohen/?originalSubdomain=ca
2. Maor Shlomo: https://www.linkedin.com/in/maor-shlomo-1088b4144/?originalSubdomain=il
3. Maksim Shaposhnikov: https://www.linkedin.com/in/maxshapp/?originalSubdomain=uk
4. Simon Maple- https://www.linkedin.com/in/simonmaple/
5. Tessl- https://www.linkedin.com/company/tesslio/
6. AI Native Dev- https://www.linkedin.com/showcase/ai-native-dev/ -
Why Faster AI Development Often Increases Rework | Cian Clarke
23 december 2025 · 54 min
Vibe coding is only good at creating a sense of progress for devs. Lees meer
In this episode of AI Native Dev, Cian Clarke, head of AI at Nearform, joins Simon Maple to talk about BMAD, their spec-driven approach that prioritizes clarity before code over prompt first development.
They also get into:
• speed upfront vs. maintainability over time
• why senior engineers gain leverage as junior pathways narrow
• the cultural shift needed to scale AI-generated software beyond solo buildersAI Native Dev, powered by Tessl and our global dev community, is your go-to podcast for solutions in software development in the age of AI. Tune in as we engage with engineers, founders, and open-source innovators to talk all things AI, security, and development.
Connect with us here:
1. Cian Clarke- https://www.linkedin.com/in/cianclarke/
2. Simon Maple- https://www.linkedin.com/in/simonmaple/
3. Tessl- https://www.linkedin.com/company/tesslio/
4. AI Native Dev- https://www.linkedin.com/showcase/ai-native-dev/ -
Building an AI Agent in 100 Lines of Code | Yaniv Aknin
16 december 2025 · 43 min
Before you add context, understand the context that’s already there. Lees meer
In this episode, Yaniv Aknin, founding engineer at Tessl, explains the built in instructions that precede every user prompt, and why acknowledging that hidden layer is critical.
On the docket:
• why tool design matters more than raw reasoning ability
• how Codex does more with fewer tools
• how subagents let Claude stay flexible under context pressure
• why generalist models may adapt better to unfamiliar toolsAI Native Dev, powered by Tessl and our global dev community, is your go-to podcast for solutions in software development in the age of AI. Tune in as we engage with engineers, founders, and open-source innovators to talk all things AI, security, and development.
Connect with us here:
1. Yaniv Aknin- https://www.linkedin.com/in/yanivaknin/
2. Simon Maple- https://www.linkedin.com/in/simonmaple/
3. Tessl- https://www.linkedin.com/company/tesslio/
4. AI Native Dev- https://www.linkedin.com/showcase/ai-native-dev/